Google Contact Lens and the Implications On Health Data


Google-smart-contact-lens-prototype-Graphic-635Technology giant Google recently revealed that they have begun testing a contact lens that is able to measure the level of glucose in tears. The Google X lab has developed this smart contact lens to help diabetics manage their blood sugar levels.

While this isn’t the first time a company has developed a contact lens of this type, it may be the first time that it gets approval in the US due to Google’s extensive network of partners. As covered in a recent prediction blog post by Stuart Nyemecz, 2014 is expected to be the year that technology will help to facilitate fully integrated healthcare – with technology playing a key role. With new challenges around both an ageing population and the increase of prolonged and costly chronic illnesses, the NHS needs to take a hard look at new technologies and options for managing chronic care issues. In addition, anything that involves real-time data could be hugely valuable in the journey to delivering a preventative healthcare model.

It is interesting to see some of the comments around the cost effective model. Real-time data analysis could potentially be expensive, the Google contact lens would need to frequently read and analyse the chemical make-up of the tears and alert the wearer if there was an issue, but on-going advancements could allow this to be achieved at a cost that is reasonable for a consumer.

This type of innovation can also potentially feed into recent NHS plans around the Academic Health Science Networks (AHSNs) with one of the aims being to deliver improved patient outcomes, and cost-efficient treatment and services innovations.  Investing in an information platform that allows for collaboration to take place both at a biomedical informatics and relationship level is key and the potential of the Google contact lens underlines quite why a wider ecosystem will need to be in place to embrace innovations of this kind that also enable healthcare providers and end users to capture, store and process medical data.

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